import torch.nn


def _dnn(input_size: int = 1,
         output_size: int = 1,
         hidden_size: int = 100,
         hidden_count: int = 3,
         activation: torch.nn.Module = torch.nn.Tanh()):
    result = torch.nn.Sequential()
    result.append(torch.nn.Linear(input_size, hidden_size))
    result.append(activation)
    for _ in range(hidden_count):
        result.append(torch.nn.Linear(hidden_size, hidden_size))
        result.append(activation)
    result.append(torch.nn.Linear(hidden_size, output_size))
    return result


def get_model():
    return _dnn(input_size=2, output_size=2)
